FastSupertrend_optim_quick5

"""
Supertrend strategy:
* Description: Generate a 3 supertrend indicators for 'buy' strategies & 3 supertrend indicators for 'sell' strategies
               Buys if the 3 'buy' indicators are 'up'
               Sells if the 3 'sell' indicators are 'down'
* Author: @juankysoriano (Juan Carlos Soriano)
* github: https://github.com/juankysoriano/
*** NOTE: This Supertrend strategy is just one of many possible strategies using `Supertrend` as indicator. It should on any case used at your own risk.
          It comes with at least a couple of caveats:
            1. The implementation for the `supertrend` indicator is based on the following discussion: https://github.com/freqtrade/freqtrade-strategies/issues/30 . Concretelly https://github.com/freqtrade/freqtrade-strategies/issues/30#issuecomment-853042401
            2. The implementation for `supertrend` on this strategy is not validated; meaning this that is not proven to match the results by the paper where it was originally introduced or any other trusted academic resources
"""

import logging
from numpy.lib import math
from freqtrade.strategy import IStrategy, IntParameter
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import pandas as pd

class FastSupertrend_optim_quick5(IStrategy):
    # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
    # It's encourage you find the values that better suites your needs and risk management strategies

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_m1": 1,
        "buy_m2": 3,
        "buy_p1": 8,
        "buy_p2": 8,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_m1": 1,
        "sell_m2": 3,
        "sell_p1": 16,
        "sell_p2": 16,
    }

    # ROI table:
    #minimal_roi = {"0": 0.1, "120": 0.025}
    minimal_roi = {"0": 0.99}

    # Stoploss:
    stoploss = -0.05
    can_short = True

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.005
    trailing_only_offset_is_reached = True

    timeframe = '1h'

    startup_candle_count = 26

    buy_m1 = IntParameter(1, 7, default=1, space='buy', load=True, optimize=True)
    buy_m2 = IntParameter(1, 7, default=3, space='buy', load=True, optimize=True)
    buy_p1 = IntParameter(7, 21, default=14, space='buy', load=True, optimize=True)
    buy_p2 = IntParameter(7, 21, default=10, space='buy', load=True, optimize=True)

    sell_m1 = IntParameter(1, 7, default=1, space='sell', load=True, optimize=True)
    sell_m2 = IntParameter(1, 7, default=3, space='sell', load=True, optimize=True)
    sell_p1 = IntParameter(7, 21, default=14, space='sell', load=True, optimize=True)
    sell_p2 = IntParameter(7, 21, default=10, space='sell', load=True, optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['supertrend_1_buy'] = self.supertrend(dataframe, self.buy_m1.value, int(self.buy_p1.value))['STX']
        dataframe['supertrend_2_buy'] = self.supertrend(dataframe, self.buy_m2.value, int(self.buy_p2.value))['STX']
        dataframe['supertrend_1_sell'] = self.supertrend(dataframe, self.sell_m1.value, int(self.sell_p1.value))['STX']
        dataframe['supertrend_2_sell'] = self.supertrend(dataframe, self.sell_m2.value, int(self.sell_p2.value))['STX']

        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=7)
        dataframe['rsi_ema'] = ta.EMA(dataframe['rsi'], timeperiod=7)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (dataframe['supertrend_1_buy'] == 'up') &
            (dataframe['supertrend_2_buy'] == 'up') &
            (dataframe['rsi_ema'] < 75) &
            (dataframe['volume'] > 0),
            'enter_long',
        ] = 1

        dataframe.loc[
            (dataframe['supertrend_1_sell'] == 'down') &
            (dataframe['supertrend_2_sell'] == 'down') &
            (dataframe['rsi_ema'] > 25) &
            (dataframe['volume'] > 0),
            'enter_short',
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['supertrend_2_sell'] == 'down'),
            'exit_long',
        ] = 1

        dataframe.loc[
            (dataframe['supertrend_2_buy'] == 'up'),
            'exit_short',
        ] = 1

        return dataframe

    """
        Supertrend Indicator; adapted for freqtrade
        from: https://github.com/freqtrade/freqtrade-strategies/issues/30
    """
    def supertrend(self, dataframe: DataFrame, multiplier, period):
        df = dataframe.copy()
        last_row = len(df)

        df['TR'] = ta.TRANGE(df)
        df['ATR'] = ta.SMA(df['TR'], period)

        st = 'ST_' + str(period) + '_' + str(multiplier)
        stx = 'STX_' + str(period) + '_' + str(multiplier)

        # Compute basic upper and lower bands
        BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
        BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values

        FINAL_UB = np.zeros(last_row)
        FINAL_LB = np.zeros(last_row)
        ST = np.zeros(last_row)
        CLOSE = df['close'].values

        # Compute final upper and lower bands
        for i in range(period, last_row):
            FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1]
            FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]

        # Set the Supertrend value
        for i in range(period, last_row):
            ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
                    FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] >  FINAL_UB[i] else \
                    FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
                    FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] <  FINAL_LB[i] else 0.00
        df_ST = pd.DataFrame(ST, columns=[st])
        df = pd.concat([df, df_ST],axis=1)

        # Mark the trend direction up/down
        df[stx] = np.where(
            (df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), np.NaN
        )

        df.fillna(0, inplace=True)

        return DataFrame(index=df.index, data={
            'ST' : df[st],
            'STX' : df[stx]
        })